• Title/Summary/Keyword: Image Layer

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Energy Efficient Cross Layer Multipath Routing for Image Delivery in Wireless Sensor Networks

  • Rao, Santhosha;Shama, Kumara;Rao, Pavan Kumar
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1347-1360
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    • 2018
  • Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm.

The Multi-layer Fabrication and Characteristic Performance for Dark Current Reduction of Mercury Iodide (Hgl2의 누설전류 저감을 위한 다층구조 제작 및 특성 평가)

  • Kim, Kyung-Jin;Park, Ji-Koon;Kang, Sang-Sik;Cha, Byung-Youl;Cho, Sung-Ho;Kim, Jin-Yung;Mun, Chi-Ung;Nam, Sang-Hee
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2005.07a
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    • pp.388-389
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    • 2005
  • In this paper, the electric properties of mercury Iodide multi-layer samples has been investigated. We measured and analyzed their performance parameters such as the X-ray sensitivity and dark-current for a mercury Iodide multi-layer X-ray detector with a dielectric layer. The digital X-ray image detector can be constructed by integrating photoconduction multi-layer that dielectric layer has characteristics of low dark-current, high X-ray sensitivity. However this process has found to have complexity on the performance of the sample. We have investigate dielectric layer that it substitute dielectric layer for HgO(Mercury Oxide). We have employed two approaches for producing the mercury Iodide sample : 1) Physical Vapor Deposition(PVD) and 2) Particle-In-Binder(PIB). In this paper fabricated by PIB Method with thicknesses ranging from approximately 180um to 240um and we could produce high-quality samples for each technique particular application. As results, the dielectric materials such as HgO between the dielectric layer and the top electrode may reduce the dark-current of the samples. Mercury Iodide multi-layer having HgO has characteristics of low dark-current, high X-ray sensitivity and simple processing. So we can acquired a enhanced signal to noise ratio. In this paper offer the method can reduce the dark-current in the X-ray detector.

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Classification Method of Plant Leaf using DenseNet (DenseNet을 활용한 식물 잎 분류 방안 연구)

  • Park, Young Min;Gang, Su Myung;Chae, Ji Hun;Lee, Joon Jae
    • Journal of Korea Multimedia Society
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    • v.21 no.5
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    • pp.571-582
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    • 2018
  • Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

A Multi-Layer Perceptron for Color Index based Vegetation Segmentation (색상지수 기반의 식물분할을 위한 다층퍼셉트론 신경망)

  • Lee, Moon-Kyu
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.43 no.1
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    • pp.16-25
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    • 2020
  • Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

Influence of Image Sticking on Electra-Optical Characteristics in Alternating-Current Plasma Display Panels

  • Choi, J.H.;Jung, Y.;Jung, K.B.;Kim, S.B.;Choi, E.H.
    • 한국정보디스플레이학회:학술대회논문집
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    • 2003.07a
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    • pp.760-763
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    • 2003
  • We have investigated the electro-optical characteristics of image sticking in AC PDP. Although Image sticking is one of major factors to determine display quality in AC PDP, so far, it has not being reported why it is occurred and how we can prevent it. In this experiment, we have analyzed the effect of MgO protective layer and phosphor on the image sticking and we have measured the difference of firing voltage, brightness and discharge current between sticking image and normal image in AC PDP. As a result, Phosphor degradation is a more major factor than MgO protective layer and the firing voltage of gas discharge in sticking image is higher than that of normal discharge.

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Reverse Iterative Image Encryption Scheme Using 8-layer Cellular Automata

  • Zhang, Xing;Zhang, Hong;Xu, Chungen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.3397-3413
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    • 2016
  • Considering that the layered cellular automata (LCA) are naturally fit for representing image data in various applications, a novel reverse iterative image encryption scheme based on LCA is proposed. Specifically, the plain image is set as the final configuration of an 8-layer CA, and some sequences derived from a random sequence are set as the pre-final configuration, which ensure that the same plain image will never be encrypted in the same way when encrypted many times. Then, this LCA is backward evolved by following some reversible two order rules, which are generated with the aid of a newly defined T-shaped neighborhood. The cipher image is obtained from the recovered initial configuration. Several analyses and experimental results show that the proposed scheme possesses a high security level and executive performance.

CNN based Image Restoration Method for the Reduction of Compression Artifacts (압축 왜곡 감소를 위한 CNN 기반 이미지 화질개선 알고리즘)

  • Lee, Yooho;Jun, Dongsan
    • Journal of Korea Multimedia Society
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    • v.25 no.5
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    • pp.676-684
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    • 2022
  • As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

Analysis on the effect of color dispersion compensating layer in the three-dimensional/two-dimensional convertible display based on parallax barrier

  • Cho, Seong-Woo;Park, Jae-Hyeung;Lee, Byoung-Ho
    • 한국정보디스플레이학회:학술대회논문집
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    • 2005.07b
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    • pp.1599-1602
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    • 2005
  • In a three-dimensional/two-dimensional convertible parallax barrier display, an additional layer compensating the color dispersion for three-dimensional display can distort displayed image in the two-dimensional mode. We analyze the effect of the color dispersion compensating layer on two-dimensional image by computer simulations.

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Parallel Synthesis Algorithm for Layer-based Computer-generated Holograms Using Sparse-field Localization

  • Park, Jongha;Hahn, Joonku;Kim, Hwi
    • Current Optics and Photonics
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    • v.5 no.6
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    • pp.672-679
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    • 2021
  • We propose a high-speed layer-based algorithm for synthesizing computer-generated holograms (CGHs), featuring sparsity-based image segmentation and computational parallelism. The sparsity-based image segmentation of layer-based three-dimensional scenes leads to considerable improvement in the efficiency of CGH computation. The efficiency enhancement of the proposed algorithm is ascribed to the field localization of the fast Fourier transform (FFT), and the consequent reduction of FFT computational complexity.

Effects of the Inlet Boundary Layer Thickness and the Boundary Layer Fence on the Heat Transfer Chracteristics in a Turbine Cascade (입구경계층 두께와 경계층 펜스가 터빈 캐스케이드내 열전달 특서에 미치는 영향)

  • Jeong, J.S.;Chung, J.T.
    • Proceedings of the KSME Conference
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    • 2001.06d
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    • pp.765-770
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    • 2001
  • The objective of the present study is to investigate the effects of the various inlet boundary layer thickness on convective heat transfer distribution in a turbine cascade endwall and blade suction surface. In addition, the proper height of the boundary layer fences for various inlet boundary layer thickness were applied to turbine cascade endwall in order to reduce the secondary flow, and to verify its influence on the heat transfer process within the turbine cascade. Convective heat transfer distributions on the experimental regions were measured by the image processing system. The results show that heat transfer coefficients on the blade suction surface were increased with an augmentation of inlet boundary layer thickness. However, in a turbine cascade endwall, magnitude of heat transfer coefficients did not change with variation of inlet boundary layer thickness. The results also present that the boundary layer fence is effective in reducing heat transfer on the suction surface. On the other hand, in the endwall region, boundary layer fence brought about the subsidiary heat transfer increment.

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